Project by Indian Society of Management Accountants with Group of MBA Students www.cmaonline.in
ISMA Project as part of students training program to develop skills
An exemplary approach towards strong Academia Industry Partnership
A method that uses measurable, historical data observations, to make forecasts by calculating the weighted average of the current period’s actual value and forecast, with a trend adjustment added in
This lesson begin with explaining the simple exponential smoothing method characteristics, and uses. Simple exponential smoothing method attempts to best fit a smoothing constant to past data. Using an example and the forecasting process, we apply the simple exponential smoothing method to create a model and forecast based upon it.
Companies are re-imagining their business in a digital avatar. Data is the new literacy of our age. How are professionals who are not data scientists to contributed? My talk shows how people can enable digital transformation no matter where they are in an organization. You do not have to know coding to thrive in the digital age.
Given at Monsanto India's Women's Network, Prasiti.
Project by Indian Society of Management Accountants with Group of MBA Students www.cmaonline.in
ISMA Project as part of students training program to develop skills
An exemplary approach towards strong Academia Industry Partnership
A method that uses measurable, historical data observations, to make forecasts by calculating the weighted average of the current period’s actual value and forecast, with a trend adjustment added in
This lesson begin with explaining the simple exponential smoothing method characteristics, and uses. Simple exponential smoothing method attempts to best fit a smoothing constant to past data. Using an example and the forecasting process, we apply the simple exponential smoothing method to create a model and forecast based upon it.
Companies are re-imagining their business in a digital avatar. Data is the new literacy of our age. How are professionals who are not data scientists to contributed? My talk shows how people can enable digital transformation no matter where they are in an organization. You do not have to know coding to thrive in the digital age.
Given at Monsanto India's Women's Network, Prasiti.
Recent Trends in Modern Operations ManagementShuhab Tariq
This paper aims to explore the recent trends in modern Operations Management aiming at a better understanding of the current developments in the area. Discussing the general picture of Operations Management, this paper aims to highlight the most important and popular trends at the moment.
The paper will discuss the Lean Operations and JIT as one of the most important trend in great detail. With the help of several examples, the paper will endeavour to find out how the concept of lean is drastically affecting the way Operations Management is conceived.
Sales Forecasting
Sales forecasting is the process of a company predicting what its future sales will be. This forecast is done for a particular period of time in the near future, usually the next fiscal year. Accurate sales forecasting enables a company to make informed business decisions. Sales forecasting is easier for established companies that have been operating for a few years than for newer companies. Established companies have years of sales records and can base their forecasts on that past sales data. Newly founded companies have to base their forecasts on less verified information, such as market research and competition analysis to forecast their future business.
Why is Sales Forecasting important?
Sales Forecasting gives insight on whether a company should expand, information about cash flow, and the ability to effectively manage its resources. Without forecasting, a company would be unsure of what inventory level to maintain, unsure on how it should allocate resources across the company, and it would have a hard time predicting future success. Forecasting sales is a crucial business practice, because in addition to helping a company allocate its internal resources effectively, having this data is important for acquiring investment capital. Often, investors want to know what a company’s future expected sales are before making an investment.
What is Forecasting?
Forecasting is a technique of predicting the future based on the results of previous data. It involves a
detailed analysis of past and present trends or events to predict future events. It uses statistical tools and
techniques. Therefore, it is also called Statistical analysis. In other words, we can say that forecasting acts
as a planning tool that helps enterprises to get ready for the uncertainty that can occur in the future.
Forecasting begins with management's experience and knowledge sharing. To obtain the most numerous
advantages from forecasts, organizations must know the different forecasting methods' more subtle
details. Also, understand what an appropriate forecasting method type can and cannot do, and realize
what forecast type is best suited to a specific need. Let's list down some significant benefits of forecasting:
• Better utilization of resources
• Formulating business plans
• Enhance the quality of management
• Helps in establishing a new business model
• Helps in making the best managerial decisions
A set of observations taken at a particular period of time. For example, having a set of login details at
regular interval of time of each user can be categorized as a time series. Click to explore about, Anomaly
Detection with Time Series Forecasting
What is Prediction?
Prediction is using the data to compute the Outcome of the unseen data.
How does Prediction work?
Firstly, the daily data is fetched from the market once at a time in a day and update it into the database.
Now, the prediction cycle along with learning developed with the use of newly combined data. Historical
data collected and the learning and prediction cycle developed to generate the results. The prediction
results obtained in the form of the various set of periods such as two days, four days, 14 days and so on.
Difference between Prediction and Forecasting
Prediction is the process of estimating the outcomes of unseen data. Forecasting is a sub-discipline of
prediction in which we use time-series data to make forecasts about the future. As a result, the only
distinction between prediction and forecasting is that we consider the temporal dimension. Confusing?
So do we forecast the weather or predict the weather? Consider this, What are the chances that it will
continue to rain in five minutes if it is already raining? Since it is raining right now, regardless of any other
factors that affect the weather (such as air pressure and temperature), the chances of it raining again in
five minutes are high. Right?vThe temporal dimension is whether it is raining right now or not? Without
that forecasting the next 5 mins wouldn't make much sense.
Time-Series refers to data recording at regular intervals of time. Click to explore about, Time Series
Forecasting Analysis
Why Forecasting is important?
Prediction of labor, material and other resources are highly crucial for operating. If the services are
Predicting better, then balanced
2. Forecasting-Process Forecasts are estimates of timing and magnitude of the occurrence of future events Key functions: An estimation tool A way of addressing the complex and uncertain environment surrounding business decision making. A tool for predicting events related to operations, planning and control. A vital perquisite for the planning process in organizations.
3. Why do we forecast… Dynamic and complex environment Short term fluctuations in production Better materials management Rationalize man-power decisions Basis for planning and scheduling Strategic decisions
4. Focus-Forecasting-Introduction Bernie Smith- Servistardivision of TruValue Two Principle theory All complex forecasting models are not always better than simpler ones. No single technique for products and services Simple techniques that work on past data also helps in developing forecasts about future as well Reasonable approach for short term (period less than a year)
12. Contd.. For items with an irregular demand history Sales for the item in the next quarter will be half of actual sales over the last 6 months F = Forecast for the item over the next quarter Q1 = Actual Sales over the most recent 3 months° (example : 100) °the first quarter in the past counting backwards from now Q2 = Actual Sales over the 3 months before that ° (example : 150 ) ° the second quarter in the past counting backwards from now ( Q1 + Q2/2)=(100 + 150)/2 = 125 for the next quarter This formula generates a reasonable forecast despite a demand history needing to be corrected
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14.
15. Helps people in forecasting seasonality, trends, items with sporadic history and other demand conditions
16. Selects the best option which results in the least error out of varied forecasting models
18. It can even include methods like exponential smoothing, if desired
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20. Ad hoc system with no theoretical basis to aid analysis or understanding.
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22. Contd… process involves the recognition of demand netting of those requirements against available and scheduled quantities generation of recommendations to meet those requirements proceeds top-down through the bill of material
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24. Contd.. Considers alternate forecast scenarios, and so the forecasts are stated in a Master Demand Schedule (MDS) determine how much demand satisfied from existing stock or existing orders Oracle Demand Planning generates forecast data. Oracle Inventory and Master Scheduling/MRP provide basic methods to generate forecasts from historical data one forecast typically contains multiple items; each item has multiple entries. For ease of use and to control forecast consumption, forecasts are grouped into forecast sets and forecasts and forecasts sets are identified by unique names
25. Generating Forecasts from Historical Information Statistical Forecasts: can span multiple periods and can recognize trend and seasonality. Focus Forecasts: examines five different forecast models against past history, determines the model that would best have predicted the history, and uses that model to generate a forecast for the current period
26. Generating Forecasts defining a forecast rule forecast method (statistical or focus), and the sources of demand in the Generate Forecast window Name of the forecast you want to populate. Forecast rule Selection criteria to identify the items An overwrite option Start date and cutoff date Post this Oracle uses Open Forecast interface and forecast entries API to integrate with other systems
